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“I Didn’t Lie, I Misspoke”: Voters’ Responses to Questionable Campaign Claims

Published online by Cambridge University Press:  25 July 2019

Elizabeth N. Simas  and
Doug Murdoch
Elizabeth N. Simas*
Affiliation:
Department of Political Science, University of Houston, 3551 Cullen Blvd., Houston, TX77204-3011, USA, Twitter: @beth_simas; e-mail: dmurdoch@uh.edu
Doug Murdoch
Affiliation:
Department of Political Science, University of Houston, 3551 Cullen Blvd., Houston, TX77204-3011, USA, Twitter: @beth_simas; e-mail: dmurdoch@uh.edu
*
*Corresponding author. Email: ensimas@uh.edu
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Abstract

Lies and half-truths are commonplace in US politics. While there is a growing literature examining questionable statements, relatively little attention has been given to the consequences that befall the sources. We address this gap by looking at how a candidate’s sex shapes citizens’ reactions to a factually dubious statement. We argue and show that subjects from the opposing party display a greater desire and tendency to punish a female candidate. Subjects from the candidate’s same party, however, appear to be more forgiving when the candidate is portrayed as a woman versus a man. In total, our findings suggest that gender and partisan biases may operate in tandem to both help and harm female political candidates who “misspeak.”

Keywords

Public opinioncandidate evaluationcampaignsgender stereotypesmisinformation
Type
Research Article
Information
Journal of Experimental Political Science , Volume 7 , Issue 2 , Summer 2020 , pp. 75 - 88
Copyright
© The Experimental Research Section of the American Political Science Association 2019

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Footnotes

The authors thank the Associate Editor, the anonymous reviewers, and the participants in the University of Houston Political Misperceptions Conference held February 8–10, 2018. All mistakes are our own. There are no conflicts of interest to report. The data, code, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/GE3E8R.

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